This paper presents potential approaches that increase the energy efficiency of an in-line induction heating system for forging of an automotive crankshaft. Both heat loss reduction and optimization of process parameters are proposed scientifically in order to minimize the energy consumption and the temperature deviation in the workpiece. We applied the numerical multiobjective optimization method in conjunction with the design of experiment (DOE), mathematical approximation with metamodel, nondominated sorting genetic algorithm (GA), and engineering data mining. The results show that using the insulating covers reduces heat by an amount equivalent to 9% of the energy stored in the heated workpiece, and approximately 5.8% of the energy can be saved by process parameter optimization.

This paper presents the development of the knowledge-based neural network (KBNN) and genetic algorithm (GA) in modeling and optimization of the roll forming (RF) process of aluminum parts. The idea of a KBNN using multifidelity finite element (FE) models was developed to model the mechanical behaviors of the aluminum sheet. Initially, the less costly but less accurate FE model was used to build the response surface functions for the knowledge path of the KBNN. After that, a small number of the more accurate but expensive finite element analysis (FEA) of the high fidelity FE model were utilized in a multilayer perceptron (MLP) neural network with the prior knowledge to produce the KBNN prediction results. Two powerful optimization algorithms, the Levenberg–Marquadrt (LM) and GA, were applied to train the KBNN. The trained KBNN was used to perform the parametric study for investigating the effects of process parameters on the part quality. After that, the optimization of the process parameters was carried out by employing the combination of the GA and KBNN. The optimization objective was minimizing the overall damage in the aluminum part while keeping the longitudinal strain and spring back angle less than allowable limits to prevent the existence of defects. The modeling and optimization results by using the KBNN and GA were compared with the results from other methods to prove the advantages of the developed one against others.